Background of Visual Attention – Theory and Experiments

Abstract

This chapter is a continuation and an extension of the previous chapter. After the brief introduction of selective visual attention in the previous chapter, we present more details on the related knowledge, theory and experiments for visual attention. Section 2.1 describes the relevant basic properties and structures of the human visual system (HVS). In Sections 2.2 and 2.3, the widely used feature integration theory (FIT), as well as its extension (i.e. the guided search (GS) theory), is to be discussed, with the experimental confirmation available. FIT deals with bottom-up attention, while GS enables a combination of bottom-up and top-down attention. Section 2.4 further discusses the time binding theory for multi-feature integration at the neuronal level. Section 2.5 gives insight into some important issues in visual attention modelling, such as competition, normalization and frequency whitening. The final section covers statistical signal processing, which can be used for modelling visual attention alone or jointly with other principles in biology. The content in this chapter is the source of inspiration for many computational models of the human visual attention. It will help the reader not only to understand the existing computational attention models to be presented throughout this book, but also to build new systems because some crucial aspects of visual attention have not been incorporated in computational and engineering models yet, due to the difficulties in modelling, as well as application scenarios not being explored.